Human molecular genetics · 2025
Fetal growth disorders detection during first trimester gestation through comprehensive maternal circulating DNA profiling.
СтатьяНазвание и аннотация: Перевод в очереди
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Cortese Rene, Cataldo Kylie, Hummel Justin, Smith Gracie, Ortega Madison, Richey Madison, Winn Hung, Gozal David, Goodman Jean Ricci
Аннотация
BACKGROUND: Early diagnosis, close follow-up and timely delivery constitute the main elements for appropriate detection and management of Fetal Growth Disorders (FGD). We hypothesized that fetoplacental FGD-associated alterations can be detected in circulating DNA (cirDNA) samples isolated from maternal blood, as early as the first gestational trimester.
METHODS: Plasma cirDNA was isolated from samples prospectively collected during first trimester gestation (n = 56). Small, Large and Appropriate for Gestational Age (SGA n = 11, LGA n = 18, and AGA n = 29, respectively) status was determined at birth according to weight and gestational age. cirDNA amount, fragmentation, mitochondrial/nuclear ratio and cirDNA methylation profiles were quantified using qPCR-based assays. Machine learning approaches were applied to build a molecular signature for prediction of LGA and SGA. Prediction accuracy was assessed by Receiver-Operating Curve (ROC) analysis, and Positive and Negative Predictive values (PPV and NPV, respectively) were calculated.
RESULTS: Total concentration of plasma cirDNA, cirDNA fragmentation and ratio of mitochondrial/nuclear cirDNA were increased in SGA and LGA compared to AGA pregnancies. Out of the 10 selected loci, we detected 5 genes (HSD2, RASSF1, CYP19A1, IL10, and LEP) showing significant differential methylation differences (p < 0.05) across the SGA, AGA and LGA samples at first trimester. cirDNA marker signature discriminated between FGD and AGA pregnancies with high accuracy (AUC > 0.95), achieving 88.8% PPV and 85.7% NPV.
CONCLUSIONS: Maternal blood cirDNA profiles accurately detects early gestation FGD. The proposed novel marker panel hold great potential for implementation of low invasive approaches for reliable prediction of FGDs, enabling a disruptive path toward precision medicine in FGD.
METHODS: Plasma cirDNA was isolated from samples prospectively collected during first trimester gestation (n = 56). Small, Large and Appropriate for Gestational Age (SGA n = 11, LGA n = 18, and AGA n = 29, respectively) status was determined at birth according to weight and gestational age. cirDNA amount, fragmentation, mitochondrial/nuclear ratio and cirDNA methylation profiles were quantified using qPCR-based assays. Machine learning approaches were applied to build a molecular signature for prediction of LGA and SGA. Prediction accuracy was assessed by Receiver-Operating Curve (ROC) analysis, and Positive and Negative Predictive values (PPV and NPV, respectively) were calculated.
RESULTS: Total concentration of plasma cirDNA, cirDNA fragmentation and ratio of mitochondrial/nuclear cirDNA were increased in SGA and LGA compared to AGA pregnancies. Out of the 10 selected loci, we detected 5 genes (HSD2, RASSF1, CYP19A1, IL10, and LEP) showing significant differential methylation differences (p < 0.05) across the SGA, AGA and LGA samples at first trimester. cirDNA marker signature discriminated between FGD and AGA pregnancies with high accuracy (AUC > 0.95), achieving 88.8% PPV and 85.7% NPV.
CONCLUSIONS: Maternal blood cirDNA profiles accurately detects early gestation FGD. The proposed novel marker panel hold great potential for implementation of low invasive approaches for reliable prediction of FGDs, enabling a disruptive path toward precision medicine in FGD.